Evidence map›Paper›PMID 30868984›Full record

ArticleInfection control and hospital epidemiology2019

A diagnostic algorithm for the surveillance of deep surgical site infections after colorectal surgery.

Tessa Mulder, Marjolein F Q Kluytmans-van den Bergh, Maaike S M van Mourik, Jannie Romme, Rogier M P H Crolla, Marc J M Bonten, Jan A J W Kluytmans

Open access · hybridAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Infection control and hospital epidemiology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed, 1 pooled it
1.5field-weighted citation impact, top 18% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed, 1 synthesis or guideline pooled it, 14 citations in OpenAlex.

  1. Pooled it
  2. Article
  3. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors at 2 institutions in 1 country.

Tessa MulderJulius Center for Health Sciences and Primary Care,University Medical Center Utrecht, University of Utrecht,Utrecht,The Netherlands.ORCID 0000-0003-4556-0338
Marjolein F Q Kluytmans-van den BerghJulius Center for Health Sciences and Primary Care,University Medical Center Utrecht, University of Utrecht,Utrecht,The Netherlands.
Maaike S M van MourikDepartment of Medical Microbiology,University Medical Center Utrecht, University of Utrecht,Utrecht,The Netherlands.
Jannie RommeDepartment of Infection Control,Amphia Hospital,Breda,The Netherlands.
Rogier M P H CrollaDepartment of Surgery,Amphia Hospital,Breda,The Netherlands.
Marc J M BontenJulius Center for Health Sciences and Primary Care,University Medical Center Utrecht, University of Utrecht,Utrecht,The Netherlands.
Jan A J W KluytmansJulius Center for Health Sciences and Primary Care,University Medical Center Utrecht, University of Utrecht,Utrecht,The Netherlands.
Utrecht University · NLAmphia Ziekenhuis · NL

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveSurveillance of surgical site infections (SSIs) is important for infection control and is usually performed through retrospective manual chart review. The aim of this study was to develop an algorithm for the surveillance of deep SSIs based on clinical variables to enhance efficiency of surveillance.

designRetrospective cohort study (2012-2015).

settingA Dutch teaching hospital.

participantsWe included all consecutive patients who underwent colorectal surgery excluding those with contaminated wounds at the time of surgery. All patients were evaluated for deep SSIs through manual chart review, using the Centers for Disease Control and Prevention (CDC) criteria as the reference standard. ANALYSIS: We used logistic regression modeling to identify predictors that contributed to the estimation of diagnostic probability. Bootstrapping was applied to increase generalizability, followed by assessment of statistical performance and clinical implications.

resultsIn total, 1,606 patients were included, of whom 129 (8.0%) acquired a deep SSI. The final model included postoperative length of stay, wound class, readmission, reoperation, and 30-day mortality. The model achieved 68.7% specificity and 98.5% sensitivity and an area under the receiver operator characteristic (ROC) curve (AUC) of 0.950 (95% CI, 0.932-0.969). Positive and negative predictive values were 21.5% and 99.8%, respectively. Applying the algorithm resulted in a 63.4% reduction in the number of records requiring full manual review (from 1,606 to 590).

conclusionsThis 5-parameter model identified 98.5% of patients with a deep SSI. The model can be used to develop semiautomatic surveillance of deep SSIs after colorectal surgery, which may further improve efficiency and quality of SSI surveillance.

Indexed as

AlgorithmsAdultAgedAged, 80 and overClinical AuditColorectal SurgeryFemaleHospitals, TeachingHumansLogistic ModelsMaleMiddle AgedNetherlandsPublic Health SurveillanceRetrospective StudiesSurgical Wound Infection

Identifiers

PMID30868984
PMCPMC6536899
OpenAlexW2922032246

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.